De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet.

Winkler S, Winkler I, Figaschewski M, Tiede T, Nordheim A, Kohlbacher O

DOI: 10.1186/s12859-022-04670-6
Published: 2022 Apr 19
Container: BMC bioinformatics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1186/s12859-022-04670-6,
  title = {De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet.},
  author = {Winkler S and Winkler I and Figaschewski M and Tiede T and Nordheim A and Kohlbacher O},
  year = {2022},
  journal = {BMC bioinformatics},
  doi = {10.1186/s12859-022-04670-6},
  url = {https://doi.org/10.1186/s12859-022-04670-6}
}

RIS

TY  - JOUR
TI  - De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet.
AU  - Winkler S
AU  - Winkler I
AU  - Figaschewski M
AU  - Tiede T
AU  - Nordheim A
AU  - Kohlbacher O
PY  - 2022
JO  - BMC bioinformatics
DO  - 10.1186/s12859-022-04670-6
UR  - https://doi.org/10.1186/s12859-022-04670-6
ER  -

APA

S, W., I, W., M, F., T, T., A, N., & O, K. (2022). De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet.. BMC bioinformatics. https://doi.org/10.1186/s12859-022-04670-6

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pubmed · retrieved 2026-09-26T12:13:11.794Z